Abstract Many problems solved by multilayer neural networks (MLNNs) are reduced into pattern mapping. If the mapping includes several different rules, it is difficult to solve these problems by using a single MLNN with linear connection weights and continuous activation functions. In this paper, a structure trainable neural network has been proposed. The gate units are embedded, which can be tr...

Phaf, Den Dulk, Tijsseling & Lebert (2001) described a self-organizing variant of the CALM modular neural network model (Murre, Phaf, & Wolters, 1992), which has been coined CALMMap. The selforganization mechanism is driven by a specific inhibition gradient formula. The disadvantage of this formula is that it introduces new parameters and is not optimally adjusted to the size of a module. This ...

This paper presents a new modular neural network architecture that is used to build a system for pattern recognition based on the iris biometric measurement of persons. In this system, the properties of the person iris database are enhanced with image processing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the n...

Cognitive approach through the neural network “NN” paradigm is a critical discipline that will help bring about autonomic computing “AC.” NN-related research, some involving new ways to apply control theory and control laws, can provide insight into how to run complex systems that optimize to their environments. NN is one kind of AC system that can embody human cognitive powers and that can ada...

This paper presents a neural network approach to the problem of nding the dialogue act for a given utterance. So far only symbolic, decision tree and statistical approaches were utilized to deal with a corpus as large as the VERBMOBIL corpus. We propose solutions to the questions of representing speech, network architecture and training in this context. We argue that, when using neural networks...

This paper reports on an experimental approach to find a modularized artificial neural network solution for the UCI letters recognition problem. Our experiments have been carried out in two parts. We investigate directed task decomposition using expert knowledge and clustering approaches to find the subtasks for the modules of the network. We next investigate processes to combine the modules ef...

Modular neural networks have the possibility of overcoming common scalability and interference problems experienced by fully connected neural networks when applied to large databases. In this paper we trial an approach to constructing modular ANN's for a very large problem from CEDAR for the classification of handwritten characters. In our approach, we apply progressive task decomposition metho...

This paper describes reactive neural control used to generate phototaxis and obstacle avoidance behavior of walking machines. It utilizes discrete-time neurodynamics and consists of two main neural modules: neural preprocessing and modular neural control. The neural preprocessing network acts as a sensory fusion unit. It filters sensory noise and shapes sensory data to drive the corresponding r...

Mental nerve neuropathy (MNN) or "numb chin syndrome" is a rare neurologic symptom most often associated with malignancy. Patients typically develop paresthesia or numbness localized to the chin and lower lip and will often seek care at their local emergency department. Pain and expansion of the lower jaw may also be present. We report a case of MNN associated with a metastatic lesion in the ma...

In this thesis we present both a novel neural network paradigm and an approach for solving sensing and control tasks for mobile robots using this neural network paradigm. Real world tasks have driven the evolution of this methodology and its components, and we apply our methodology successfully to two robotics applications. We conclude that for some tasks, our novel modular neural network appro...